Publications – Aimed

Article Title	Journal Article	Authors	Abstract	Page Number/Volume Number	Status	URL	Published Date
PETS: A Pharmacological Effects of Targets Simulator towards Quantitative Evaluation of Drug Repositioning Candidates		Thanh Nguyen, Sara Ibrahim, and Jake Y. Chen*.			Under preparation
A Novel in vivo Model of Glioblastoma Radiation Resistance Identifies Long Non-coding RNAs and Targetable Kinases		Christian T. Stackhouse, Joshua C. Anderson, Zongliang Yue, Thanh Nguyen, Nicholas J. Eustace, Lara Ianov, Catherine P. Langford, Jelai Wang, James R. Rowland III, Chuan Xing, Fady M. Mikhail, Eddy S. Yang, Anita B. Hjelmeland, C. Ryan Miller, Jake Y. Chen, G. Yancey Gillespie, Christopher D. Willey			Submitted
A Commensurate Feature Selection Method in Data Preprocessing for Classification	IEEE Transactions on Knowledge and Data Engineering	Nuo Xu, Xuan Huang, Thanh Nguyen, and Jake Y. Chen*			Under Revision
STAT3 signaling contributes to cellular metabolism changes of drug-persistent leukemic stem cells	Leukemia	Sweta B. Patel, Travis Nemkov, Davide Stefanoni, Gloria A. Benavides, Brittany L. Crown, Victoria R. Matkins, Virginia Camacho, Ashley T. Hoang, Danielle E. Tenen, Samuel L. Wolock, Jihye Park, Mahmoud A. Bassal, Li Ying, Zongliang Yue, Jake Y. Chen, Henry Yang, Daniel G. Tenen, Paul Brent Ferrell, Rui Lu, Victor Darley-Usmar, Angelo D’Alessandro, Ravi Bhatia, Robert S. Welner			Under Revision
High Dimensional Analysis Reveals IL-4R as a Protective Factor to Prevent DN2 B-cell Development in SLE	J. Immunology	Min Gao, Shanrun Liu, Qi Wu, PingAr Yang, Alex Essman, Bao Luo, Oluwagbemiga A. Ojo, David Crossman, Michael R. Crowley, Ignacio Sanz, Jake Y. Chen, W. Winn Chatham, John D. Mountz, and Hui-Chen Hsu.		IF = 4.34	Under Revision
Vitamin D and Lumisterol Derivatives Can Act on Liver X Receptors (LXRs)	Scientific Reports	Andrzej T Slominski, Tae-Kang Kim, Shariq Qayyum, Yuwei Song, Zorica Janjetovic, Allen SW Oak, Radomir M Slominski, Chander Raman, Joanna Stefan, Carlos A Mier-Aguilar, Venkatram Atigadda, David K Crossman, Andriy Golub, Yaroslav Bilokin, Edith K Y Tang, Jake Y. Chen, Robert C Tuckey, Anton M Jetten, Yuhua Song	The interactions of derivatives of lumisterol (L3) and vitamin D3 (D3) with liver X receptors (LXRs) were investigated. Molecular docking using crystal structures of the ligand binding domains (LBDs) of LXRα and β revealed high docking scores for L3 and D3 hydroxymetabolites, similar to those of the natural ligands, predicting good binding to the receptor. RNA sequencing of murine dermal fibroblasts stimulated with D3-hydroxyderivatives revealed LXR as the second nuclear receptor pathway for several D3-hydroxyderivatives, including 1,25(OH)2D3. This was validated by their induction of genes downstream of LXR. L3 and D3-derivatives activated an LXR-response element (LXRE)-driven reporter in CHO cells and human keratinocytes, and by enhanced expression of LXR target genes. L3 and D3 derivatives showed high affinity binding to the LBD of the LXRα and β in LanthaScreen TR-FRET LXRα and β coactivator assays. The majority of metabolites functioned as LXRα/β agonists; however, 1,20,25(OH)3D3, 1,25(OH)2D3, 1,20(OH)2D3 and 25(OH)D3 acted as inverse agonists of LXRα, but as agonists of LXRβ. Molecular dynamics simulations for the selected compounds, including 1,25(OH)2D3, 1,20(OH)2D3, 25(OH)D3, 20(OH)D3, 20(OH)L3 and 20,22(OH)2L3, showed different but overlapping interactions with LXRs. Identification of D3 and L3 derivatives as ligands for LXRs suggests a new mechanism of action for these compounds.		Accepted	https://pubmed.ncbi.nlm.nih.gov/33850196/	April 13, 2021
Linking Clinotypes to Phenotypes and Genotypes from Laboratory Test Results in Comprehensive Physical Exams.	BMC Medical Informatics and Decision Making	Thanh Nguyen, Tongbin Zhang, Geoffrey Fox, Sisi Zeng, Ni Cao, Chuandi Pan, and Jake Y. Chen.	In this work, we aimed to demonstrate how to utilize the lab test results and other clinical information to support precision medicine research and clinical decisions on complex diseases, with the support of electronic medical record facilities. We defined “clinotypes” as clinical information that could be observed and measured objectively using biomedical instruments. From well-known ‘omic’ problem definitions, we defined problems using clinotype information, including stratifying patients-identifying interested sub cohorts for future studies, mining significant associations between clinotypes and specific phenotypes-diseases, and discovering potential linkages between clinotype and genomic information. We solved these problems by integrating public omic databases and applying advanced machine learning and visual analytic techniques on two-year health exam records from a large population of healthy southern Chinese individuals (size n = 91,354). When developing the solution, we carefully addressed the missing information, imbalance and non-uniformed data annotation issues.	Vol. 21, Suppl 3: 51 doi.org /10.1186/s12911-021-01387-z.	Published	https://pubmed.ncbi.nlm.nih.gov/33627109/	February 24, 2021
PAGER-CoV: A Comprehensive Collection of Pathways, Annotated-gene-lists, and Gene Signatures for Coronavirus Disease Studies	Nucleic Acids Research	Zongliang Yue, Eric Zhang, Clark Xu, Sunny Khurana, Nishant Batra, Son Dang, and Jake Y. Chen*	PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.	IF = 11.5	Published	https://pubmed.ncbi.nlm.nih.gov/33245774/	January 8, 2021
Polar Gini Curve: a Quantitative Technique to Discover Gene Expression Spatial Patterns from Single-cell Data	Genomics, Proteomics, and Bioinformatics	Thanh Nguyen, Jacob Jeevan, Nuo Xu, and Jake Y. Chen		IF = 6.60	Published
Multi-omic Exploration of Inherent and Acquired Radiation Resistance of Glioblastoma Patient-derived Xenografts	International Journal of Radiation Oncology Biology Physics	C.D. Willey, C.T. Stackhouse, J.R. RowlandIII, C.P. Langford, J.C. Anderson, L. Ianov, Z. Yue, T. Nguyen, A.B. Hjelmeland, Jake Y. Chen, G.Y. Gillespie		Vol. 108, No. 3, Suppl., pp. S40	Published
Identifying the Key Regulators that Promote Cell-cycle Activity in the Hearts of Early Neonatal Pigs after Myocardial Injury	PLOS ONE	Eric Zhang, Thanh Nguyen, Meng Zhao, Son Do Hai Dang, Jake Y. Chen, Weihua Bian, and Gregory P Walcott	Mammalian cardiomyocytes exit the cell cycle shortly after birth. As a result, an occurrence of coronary occlusion-induced myocardial infarction often results in heart failure, postinfarction LV dilatation, or death, and represents one of the most significant public health morbidities worldwide. Interestingly however, the hearts of neonatal pigs have been shown to regenerate following an acute myocardial infarction (MI) occuring on postnatal day 1 (P1); a recovery period which is accompanied by an increased expression of markers for cell-cycle activity, and suggests that early postnatal myocardial regeneration may be driven in part by the MI-induced proliferation of pre-existing cardiomyocytes. In this study, we identified signaling pathways known to regulate the cell cycle, and determined of these, the pathways persistently upregulated in response to MI injury. We identified five pathways (mitogen associated protein kinase [MAPK], Hippo, cyclic [cAMP], Janus kinase/signal transducers and activators of transcription [JAK-STAT], and Ras) which were comprehensively upregulated in cardiac tissues collected on day 7 (P7) and/or P28 of the P1 injury hearts. Several of the initiating master regulators (e.g., CSF1/CSF1R, TGFB, and NPPA) and terminal effector molecules (e.g., ATF4, FOS, RELA/B, ITGB2, CCND1/2/3, PIM1, RAF1, MTOR, NKF1B) in these pathways were persistently upregulated at day 7 through day 28, suggesting there exists at least some degree of regenerative activity up to 4 weeks following MI at P1. Our observations provide a list of key regulators to be examined in future studies targeting cell-cycle activity as an avenue for myocardial regeneration.		Published	https://pubmed.ncbi.nlm.nih.gov/32730272/	July 30, 2020
Decoding SARS-CoV-2 Hijacking of Host Mitochondria in Pathogenesis of COVID-19	American Journal of Physiology-Cell Physiology	Keshav K Singh, Gyneshwer Chaubey, Jake Y. Chen, and Prashanth Suravajhala.	Because of the ongoing pandemic around the world, the mechanisms underlying the SARS-CoV-2-induced COVID-19 are subject to intense investigation. Based on available data for the SARS-CoV-1 virus, we suggest how CoV-2 localization of RNA transcripts in mitochondria hijacks the host cell’s mitochondrial function to viral advantage. Besides viral RNA transcripts, RNA also localizes to mitochondria. SARS-CoV-2 may manipulate mitochondrial function indirectly, first by ACE2 regulation of mitochondrial function, and once it enters the host cell, open-reading frames (ORFs) such as ORF-9b can directly manipulate mitochondrial function to evade host cell immunity and facilitate virus replication and COVID-19 disease. Manipulations of host mitochondria by viral ORFs can release mitochondrial DNA (mtDNA) in the cytoplasm and activate mtDNA-induced inflammasome and suppress innate and adaptive immunity. We argue that a decline in ACE2 function in aged individuals, coupled with the age-associated decline in mitochondrial functions resulting in chronic metabolic disorders like diabetes or cancer, may make the host more vulnerable to infection and health complications to mortality. These observations suggest that distinct localization of viral RNA and proteins in mitochondria must play essential roles in SARS-CoV-2 pathogenesis. Understanding the mechanisms underlying virus communication with host mitochondria may provide critical insights into COVID-19 pathologies. An investigation into the SARS-CoV-2 hijacking of mitochondria should lead to novel approaches to prevent and treat COVID-19.	Vol. 319, No. 2	Published	https://pubmed.ncbi.nlm.nih.gov/32510973/	August 1, 2020
IL-23 promotes a coordinated B-cell germinal center program for class-switch recombination to IgG2b in BXD2 mice	J. Immunology,	Huixian Hong, Min Gao, Qi Wu, PingAr Yang, Shanrun Liu, Hao Li, Peter D. Burrows, Daniel Cua, Jake Y. Chen, Hui-Chen Hsu, and John D. Mountz	IL-23 promotes autoimmune disease, including Th17 CD4 T cell development and autoantibody production. In this study, we show that a deficiency of the p19 component of IL-23 in the autoimmune BXD2 (BXD2-p19-/- ) mouse leads to a shift of the follicular T helper cell program from follicular T helper (Tfh)-IL-17 to Tfh-IFN-γ. Although the germinal center (GC) size and the number of GC B cells remained the same, BXD2-p19-/- mice exhibited a lower class-switch recombination (CSR) in the GC B cells, leading to lower serum levels of IgG2b. Single-cell transcriptomics analysis of GC B cells revealed that whereas Ifngr1, Il21r, and Il4r genes exhibited a synchronized expression pattern with Cxcr5 and plasma cell program genes, Il17ra exhibited a synchronized expression pattern with Cxcr4 and GC program genes. Downregulation of Ighg2b in BXD2-p19-/- GC B cells was associated with decreased expression of CSR-related novel base excision repair genes that were otherwise predominantly expressed by Il17ra + GC B cells in BXD2 mice. Together, these results suggest that although IL-23 is dispensable for GC formation, it is essential to promote a population of Tfh-IL-17 cells. IL-23 acts indirectly on Il17ra + GC B cells to facilitate CSR-related base excision repair genes during the dark zone phase of GC B cell development.	Vol. 205(2), pp 346-358. (IF = 4.34)	Published	https://pubmed.ncbi.nlm.nih.gov/32554431/	July 15, 2020
Guest Editorial for Selected Papers from BIOKDD 2018 and DMBIH 2018	IEEE/ACM Transactions on Computational Biology and Bioinformatics	Da Yan, Xin Gao, Samah J. Fodeh, and Jake Y. Chen*		ol. 17, No. 6, pp. 1832-1834.	Published
Polyvalent Therapeutic Vaccine for Type 2 Diabetes Mellitus: Immunoinformatics Approach to Study Co-Stimulation of Cytokines and GLUT1 Receptors	BMC Molecular and Cell Biology	Syed A Muhammad, Hiba Ashfaq, Sidra Zafar, Fahad Munir, Muhammad B Jamshed, Jake Y. Chen, and Qiyu Zhang	Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route.	Vol. 21(1):56. doi: 10.1186/s12860-020-00279-w. (IF = 3.74)	Published	https://pubmed.ncbi.nlm.nih.gov/32703184/	July 23, 2020
Association of CMV genomic mutations with symptomatic infection and hearing loss in congenital CMV infection	BMC Infectious Disease	George Dobbins, Amit Patki, Dongquan Chen, Hemant K. Tiwari, Curtis Hendrickson, William J. Britt, Karen Fowler, Jake Y. Chen, Suresh B Boppana, and Shannon A Ross*	Congenital cytomegalovirus (cCMV) infection is the most common congenital infection and a leading cause of long-term neurological and sensory sequelae, the most common being sensorineural hearing loss (SNHL). Despite extensive research, clinical or laboratory markers to identify CMV infected children with increased risk for disease have not been identified. This study utilizes viral whole-genome next generation-sequencing (NGS) of specimens from congenitally infected infants to explore viral diversity and specific viral variants that may be associated with symptomatic infection and SNHL.	Vol. 19(1);1046. doi: 10.1186/s12879-019-4681-0. (IF=2.58)	Published	https://pubmed.ncbi.nlm.nih.gov/31822287/	December 10, 2019
WIPER: Weighted in-Path Edge Ranking for Biomolecular Association Networks	Quantitative Biology	Zongliang Yue, Thanh Nguyen, Eric Zhang, Jianyi Zhang, and Jake Y. Chen*.	In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations (“edges”), which can represent gene regulatory events essential to biological processes.	Vol. 7, pp 313-326.	Published	https://link.springer.com/content/pdf/10.1007/s40484-019-0180-y.pdf	August 2, 2019
BEERE: a Web Server for Biomedical Entity Expansion, Ranking, and Explorations. Nucleic Acids Research, gkz428 (IF = 11.6)	Nucleic Acids Research	Zongliang Yue, Christopher D Willey, Anita B Hjelmeland, and Jake Y. Chen*	BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. ‘biomedical entities’, in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity-a gene or a term-within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype-phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.	IF = 11.6	Published	https://pubmed.ncbi.nlm.nih.gov/31114876/	January 20, 2019
A GVHD-free Antitumoral Signature Following Allogeneic Donor Lymphocyte Injection Identified by Proteomics and Systems Biology	Biology. JCO Precision Oncology	Xiaowen Liu, Zongliang Yue, Yimou Cao, Lauren Taylor, Qing Zhang, Sung Wong Choi, Samir Hanash, Sawa Ito, Jake Y. Chen, Huanmei Wu, and Sophie Paczesny	As a tumor immunotherapy, allogeneic hematopoietic cell transplantation with subsequent donor lymphocyte injection (DLI) aims to induce the graft-versus-tumor (GVT) effect but often also leads to acute graft-versus-host disease (GVHD). Plasma tests that can predict the likelihood of GVT without GVHD are still needed.	Vol. 3, pp 1-11.	Published	https://pubmed.ncbi.nlm.nih.gov/31406955/	May 23, 2019
Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information	BMC Bioinformatics	Zaixiang Tang, Shufeng Lei, Xinyan Zhang, Zixuan Yi, Boyi Guo, Jake Y. Chen, Yueping Shen, and Nengjun Yi	Group structures among genes encoded in functional relationships or biological pathways are valuable and unique features in large-scale molecular data for survival analysis. However, most of previous approaches for molecular data analysis ignore such group structures. It is desirable to develop powerful analytic methods for incorporating valuable pathway information for predicting disease survival outcomes and detecting associated genes.	Vol. 20, p. 94	Published	https://pubmed.ncbi.nlm.nih.gov/30813883/	February 27, 2019
Scalable De Novo Genome Assembly Using a Pregel-Like Graph-Parallel System. ACM/IEEE Transactions on Computational Biology and Bioinformatics, doi.org/10.1109/TCBB.2019.2920912 (IF = 1.96)	ACM/IEEE Transactions on Computational Biology and Bioinformatics,	Guimu Guo, Hongzhi Chen, Da Yan, James Cheng, and Jake Y. Chen	De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throughput genome sequencing and thus accelerates the discovery of new genomes. In this paper, we present a toolkit, called PPA-assembler, for de novo genome assembly in a distributed setting. The operations in our toolkit provide strong performance guarantees, and can be assembled to implement various sequencing strategies. PPA-assembler adopts the popular de Bruijn graph based approach for sequencing, and each operation is implemented as a program in Google’s Pregel framework which can be easily deployed in a generic cluster. Experiments on large real and simulated datasets demonstrate that PPA-assembler is much more efficient than the state-of-the-arts while providing comparable sequencing quality. PPA-assembler has been open-sourced at https://github.com/yaobaiwei/PPA-Assembler.	IF = 1.96	Published	https://pubmed.ncbi.nlm.nih.gov/31180898/	April 6, 2021
A Systematic Simulation-based Meta-analytical Framework for Prediction of Physiological Biomarkers in Alopecia	Journal of Biological Research-Thessaloniki	Syed Aun Muhammad, Nighat Fatima, Rehan Zafar Paracha, Amjad Ali, and Jake Y. Chen	Alopecia or hair loss is a complex polygenetic and psychologically devastating disease affecting millions of men and women globally. Since the gene annotation and environmental knowledge is limited for alopecia, a systematic analysis for the identification of candidate biomarkers is required that could provide potential therapeutic targets for hair loss therapy.	Vol. 26, No. 2	Published	https://pubmed.ncbi.nlm.nih.gov/30993080/	April 4, 2019
Multiscale and Multimodal Analysis for Computational Biology	IEEE/ACM Transactions on Computational Biology and Bioinformatics	Xin Gao, Jake Y. Chen, and Mohammed J. Zaki		Vol. 15, No. 6, pp. 1951-1952 (IF = 1.96)	Published		January 20, 2018
Super Gene Set” Causal Relationship Discovery from Functional Genomics Data. ACM/IEEE Transactions on Computational Biology and Bioinformatics, Vol. 15, No. 6, pp. 1991-1998. (IF = 1.96)	ACM/IEEE Transactions on Computational Biology and Bioinformatics	Zongliang Yue, Michael T. Neylon, Thanh Nguyen, Timothy Ratliff, Jake Y. Chen*	In this article, we present a computational framework to identify “causal relationships” among super gene sets. For “causal relationships,” we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to “pathways, annotated lists, and gene signatures,” or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.	Vol. 15, No. 6, pp	Published	https://pubmed.ncbi.nlm.nih.gov/30040650/	July 23, 2018
Regenerative Potential of Neonatal Porcine Hearts	Circulation	Wuqiang Zhu, Eric Zhang, Meng Zhao, Zechen Chong, Chengming Fan, Yawen Tang, Jervaughn D. Hunter, Anton V. Borovjagin, Greggory P. Walcott, Jake Y. Chen, Gangjian Qin, Jianyi Zhang	Rodent hearts can regenerate myocardium lost to apical resection or myocardial infarction for up to 7 days after birth, but whether a similar window for myocardial regeneration also exists in large mammals is unknown.	Vol. 138, No. 24, pp. 2809-2816. (IF = 18.88)	Published	https://pubmed.ncbi.nlm.nih.gov/30030418/	December 11, 2018
Simulation Study of cDNA Dataset to Investigate Possible Association of Differentially Expressed Genes of Human THP1-Monocytic Cells in Cancer Progression Affected by Bacterial Shiga Toxins	Frontiers in Microbiology	Syed Aun Muhammad, Jinlei Guo, Thanh Minh Nguyen, Xiaogang Wu, Baogang Bai, X Frank Yang, Jake Y. Chen	Shiga toxin (Stxs) is a family of structurally and functionally related bacterial cytotoxins produced by Shigella dysenteriae serotype 1 and shigatoxigenic group of Escherichia coli that cause shigellosis and hemorrhagic colitis, respectively. Until recently, it has been thought that Stxs only inhibits the protein synthesis and induces expression to a limited number of genes in host cells, but recent data showed that Stxs can trigger several signaling pathways in mammalian cells and activate cell cycle and apoptosis. To explore the changes in gene expression induced by Stxs that have been shown in other systems to correlate with cancer progression, we performed the simulated analysis of cDNA dataset and found differentially expressed genes (DEGs) of human THP1-monocytic cells treated with Stxs. In this study, the entire data (treated and untreated replicates) was analyzed by statistical algorithms implemented in Bioconductor packages. The output data was validated by the k-fold cross technique using generalized linear Gaussian models. A total of 50 DEGs were identified. 7 genes including TSLP, IL6, GBP1, CD274, TNFSF13B, OASL, and PNPLA3 were considerably (<0.00005) related to cancer proliferation. The functional enrichment analysis showed 6 down-regulated and 1 up-regulated genes. Among these DEGs, IL6 was associated with several cancers, especially with leukemia, lymphoma, lungs, liver and breast cancers. The predicted regulatory motifs of these genes include conserved RELA, STATI, IRFI, NF-kappaB, PEND, HLF, REL, CEBPA, DI_2, and NFKB1 transcription factor binding sites (TFBS) involved in the complex biological functions. Thus, our findings suggest that Stxs has the potential as a valuable tool for better understanding of treatment strategies for several cancers.	Vol. 9, No. 380	Published	https://pubmed.ncbi.nlm.nih.gov/29593668/	March 13, 2018
PAGER 2.0: An Update to the Pathway, Annotated-list, and Gene-signature Electronic Repository for Network Biology,	Nucleic Acids Research	Zongliang Yue, Qi Zheng, Micheal T Neylon, Minjae Yoo, Jimin Shin, Zhiying Zhao, Aik Choon Tan, and Jake Y. Chen	Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.	Vol. 46, No. D1, pp. D668-D676. (IF = 10.0)	Published	https://pubmed.ncbi.nlm.nih.gov/29126216/	January 4, 2018
Repositioning Drugs by Targeting Network Modules: A Parkinson’s Disease Case Study	BMC Bioinformatics	Zongliang Yue, Itika Arora, Eric Zhang, Vincent Laufer, S. Louis Bridges, and Jake Y. Chen*		Vol. 18, Suppl 14, 532. (IF = 2.75)	Published	https://pubmed.ncbi.nlm.nih.gov/29297292/	December 28, 2017
Characterization and Analysis of Long Non-Coding RNA (lncRNA) in in vitro- and ex vivo-derived Cardiac Progenitor Cells	PLOS ONE	Baron Arnone, Jake Y. Chen, and Gangjian Qin	Recent advancements in cell-based therapies for the treatment of cardiovascular disease (CVD) show continuing promise for the use of transplanted stem and cardiac progenitor cells (CPCs) to promote cardiac restitution. However, a detailed understanding of the molecular mechanisms that control the development of these cells remains incomplete and is critical for optimizing their use in such therapy. Long non-coding (lnc) RNA has recently emerged as a crucial class of regulatory molecules involved in directing a variety of critical biological processes including development, homeostasis and disease. As such, a rising body of evidence suggests that they also play key regulatory roles in CPC development, though many questions remain regarding the expression landscape and specific identity of lncRNA involved in this process. To address this, we performed whole transcriptome sequencing of two murine CPC populations-Nkx2-5 EmGFP reporter-sorted embryonic stem (ES) cell-derived and ex vivo, cardiosphere-derived-in an effort to characterize their lncRNA profiles and potentially identify novel CPC regulators. The resulting sequencing data revealed an enrichment in both CPC populations for a panel of previously-identified lncRNA genes associated with cardiac differentiation. Additionally, a total of 1,678 differentially expressed and as-of-yet unannotated, putative lncRNA genes were found to be enriched for in the two CPC populations relative to undifferentiated ES cells.	Vol. 12, No. 6, e0180096. (IF = 3.53)	Published	https://pubmed.ncbi.nlm.nih.gov/28640894/	June 22, 2017
Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases	Rheumatic Disease Clinics	Vincent A. Laufer, Jake Y. Chen, S Louis Bridges Jr	The use of high-throughput omics may help to understand the contribution of genetic variants to the pathogenesis of rheumatic diseases. We discuss the concept of missing heritability: that genetic variants do not explain the heritability of rheumatoid arthritis and related rheumatologic conditions. In addition to an overview of how integrative data analysis can lead to novel insights into mechanisms of rheumatic diseases, we describe statistical approaches to prioritizing genetic variants for future functional analyses. We illustrate how analyses of large datasets provide hope for improved approaches to the diagnosis, treatment, and prevention of rheumatic diseases.	Vol. 43, No. 3, pp. 449-466. (IF = 1.98)	Published	https://pubmed.ncbi.nlm.nih.gov/28711145/	August 4, 2017

Number	Name	Type	Year
1	Jake Chen*, Michael Neylon, Zongliang Yue, Thanh Nguyen, Timothy Ratliff (2016) Towards Constructing “Super Gene Sets” Regulatory Networks, Proceedings of the 2016 International Conference on Bioinformatics & Biomedicine, Shenzhen, China, doi: 10.1109/BIBM.2016.7822534.	Conference or Workshop Proceedings Articles	2016
2	Ni Cao, Sisi Zeng, Feixia Shen, Chuandi Pan, Chengshui Chen, Thanh Nguen, and Jake Chen* (2015) Predictive and Preventive Models for Diabetes Prevention using Clinical Information in Electronic Health Record, Proceedings of the 2015 International Conference on Bioinformatics & Biomedicine, Washington, DC, pp. 867-874.	Conference or Workshop Proceedings Articles	2015
3	Thanh Nguyen, Junjie Chen, Dongxiang Ji, Xiaoru Sun, Chuandi Pan,* Chengshui Chen, and Jake Y. Chen (2015) An Integrated Machine Learning and Network Analysis to Discover Marker Clinical Measurement in Lung Cancer, Proceedings of 2015 International Conference on Genome Medicine, eds. Le L & Pham S, Ho Chi Minh City, Viet Nam.	Conference or Workshop Proceedings Articles	2015
4	Chayaporn Suphavilai, Liugen Zhu, and Jake Y. Chen* (2014) Constructing Regulatory Gene Set Network to Reveal Novel Insights into Biological Systems, Proceedings of the IEEE 4th International Conference on Computational Advances in Bio and medical Sciences, Miami Beach, FL, doi: 10.1109/ICCABS.2014.6863927.	Conference or Workshop Proceedings Articles	2014
5	Xiaogang Wu and Jake Chen* (2012) An Evaluation for Merging Signaling Pathways by Using Protein-Protein Interaction Data, Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics, Washington, DC.	Conference or Workshop Proceedings Articles	2012
6	David Johnson, Brandon Shafer, Jaehwan John Lee, and Jake Chen* (2012) Multi-Biomarker Panel Selection on a GPU, Proceedings of IEEE International Conference on Electro/Information Technology, Indianapolis, IN.	Conference or Workshop Proceedings Articles	2012
7	Xiaogang Wu, Hui Huang, Tao Wei, Ragini Pandey, Christopher Reinhard, Shuyu D. Li, Jake Y. Chen* (2012) Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays, Proceedings of the Eighth Annual International Symposium on Integrative Bioinformatics, Hangfzhou, Zhejiang, China.	Conference or Workshop Proceedings Articles	2012
8	Liang-Chin Huang and Jake Y. Chen* (2011) A Network Biology Approach to Predicting Drug Cardiotoxicity, Proceedings of the International Conference on Bioinformatics & Biomedicine, Atlanta, GA, pp. 278-281.	Conference or Workshop Proceedings Articles	2011
9	Jason McLaughlin, Qian You, Shiaofen Fang, and Jake Y. Chen* (2011) TAO: Terrain Analytic Operators for Expert-Guided Data Mining Applications, Workshop on Visual Analytics in Healthcare, Providence, RI.	Conference or Workshop Proceedings Articles	2011
10	Hui Huang, Xiaogang Wu, Sara Ibrahim, Marianne Mckenzie, Sunil Badve, and Jake Y. Chen* (2011) Predicting Drug Efficacy Based on the Integrated Breast Cancer Pathway Model, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, San Antonio, TX.	Conference or Workshop Proceedings Articles	2011
11	Xiaogang Wu and Jake Y. Chen* (2011) Network Reordering Based Integrative Expression Profiling for Microarray Classification, 2011 Great Lakes Bioinformatics Conference, Athens, OH.	Conference or Workshop Proceedings Articles	2011
12	Xukai Zou, Peng Liu, and Jake Y. Chen* (2011) Personal Genome Privacy Protection with Feature-based Hierarchical Dual-stage Encryptions, 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, San Antonio, TX.	Conference or Workshop Proceedings Articles	2011
13	Fan Zhang and Jake Y. Chen* (2010) Novel Protein Isoform Biomarker Identifications from Proteomes, 2nd International Workshop on Data Mining for Biomarker Discovery at the 2010 IEEE International Conference of Bioinformatics and Biomedicine, Hong Kong.	Conference or Workshop Proceedings Articles	2011
14	Hui Huang, Xiaogang Wu, Shuyu Li, Sara Ibrahim, Taiwo Ajumobi, and Jake Y. Chen* (2010) Evaluate Drug Effects on Gene Expression Profiles with Connectivity Maps, 2nd International Workshop on Data Mining for Biomarker Discovery at the 2010 IEEE International Conference of Bioinformatics and Biomedicine, Hong Kong.	Conference or Workshop Proceedings Articles	2010
15	Fan Zhang and Jake Y. Chen* (2010) Proteome-scale Protein Isoform Characterization with High Performance Computing, 2010 Microsoft eScience Workshop Proceedings, Berkeley, CA.	Conference or Workshop Proceedings Articles	2010
16	Fengjun Li, Jake Y. Chen, Xukai Zou, and Peng Liu (2010) New Privacy Threats in Healthcare Informatics: When Medical Records Join the Web, Ninth ACM SIGKDD Workshop on Data Mining in Bioinformatics (BioKDD 2010) at the International Conference on Knowledge Discovery and Data Mining, Washington, DC. (Acceptance rate: 37%)	Conference or Workshop Proceedings Articles	2010
17	Fan Zhang, Mu Wang, and Jake Y. Chen* (2010) Breast Cancer Plasma Protein Biomarker Discovery by Coupling LC-MS/MS Proteomics and Systems Biology, Proceedings of the ACM International Conference on Bioinformatics and Computational Biology, Niagara Falls, NY. (poster paper; accepted)	Conference or Workshop Proceedings Articles	2010
18	Tianxiao Huan, Xiaogang Wu, Zengliang Bai, and Jake Y. Chen* (2010) Random Walk Ranking Guided by Disease Association Networks for Lung Cancer Biomarker Discovery, Proceedings of the International Symposium on Biocomputing, Vol. 1, No. 33, pp. 1-8. (Acceptance rate: 30%)	Conference or Workshop Proceedings Articles	2010
19	Jieun Jeong and Jake Y. Chen* (2009) RIC: Ranking with Interaction Chains and its Applications in Computational Clinical Proteomics Studies, Proceedings of the International Conference on Bioinformatics & Biomedicine, pp. 216-221. (Acceptance rate: 17% out of 256 submissions)	Conference or Workshop Proceedings Articles	2009
20	Tianxiao Huan, Xiaogang Wu, Zengliang Bai, and Jake Y. Chen* (2009) SWRWR: Seed-weighted Random Walks Ranking Method and Its Application to Leukemia Cancer Biomarker Prioritizations, IEEE International Conference on Bioinformatics and Biomedicine Workshop 2009, pp. 220-225. (Acceptance rate: <35%)	Conference or Workshop Proceedings Articles	2009
21	Fan Zhang and Jake Y. Chen* (2009) A Neural Network Approach to Multi-biomarker Panel Development Based on LC/MS/MS Proteomics Profiles: A Case Study in Breast Cancer, Proceedings of the 22nd IEEE Symposium on Computer-Based Medical Systems, pp. 1-6, doi: 10.1109/CBMS.2009.5255456.	Conference or Workshop Proceedings Articles	2009
22	Hui Huang, Jiao Li, and Jake Y. Chen* (2009) Disease Gene-fishing in Molecular Interaction Networks: a Case Study in Colorectal Cancer, Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6416-6419.	Conference or Workshop Proceedings Articles	2009
23	Xiaogang Wu, Ragini Pandey, and Jake Y. Chen* (2009) Network Topological Reordering Revealing Systemic Patterns in Yeast Protein Interaction Networks, Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6416-6419.	Conference or Workshop Proceedings Articles	2009
24	Qian You, Shiaofen Fang, Snehasis Mukhopadhyay, Harsha Vaka, and Jake Y. Chen* (2009) Visualizing a Correlative Multi-level Graph of Biology Entity Interactions, Workshop on Intelligent Biomedical Information Systems at the 12th International Conference on Network-Based Information Systems, pp.304-309.	Conference or Workshop Proceedings Articles	2009
25	John Springer and Jake Y. Chen* (2009) Toward Ontology-Driven Omics Data Integration in Current Database Management Systems, Workshop on Intelligent Biomedical Information Systems at the 12th International Conference on Network-Based Information Systems, pp. 292-297.	Conference or Workshop Proceedings Articles	2009
26	Jiao Li, Pamela Crowell, and Jake Y. Chen* (2009) Construct Anticancer Drug-Drug Correlation Network, Proceedings of the 24th Annual ACM Symposium on Applied Computing, Vol. 2, pp. 771-775. (Acceptance rate: 28%)	Conference or Workshop Proceedings Articles	2009
27	Mingyi Wang and Jake Y. Chen* (2008) Gene Selection using the GMM-IG Framework based Integrative Analysis. IEEE Proceedings of the International Conference on Biomedical Engineering and Informatics, Vol.1, pp. 292-296. (Acceptance rate: ~30%, out of >700 submissions)	Conference or Workshop Proceedings Articles	2008
28	Harini Kasamsetty, Xiaogang Wu, and Jake Y. Chen* (2008) An Integrative Human Pathway Database for Systems Biology Applications. Proceedings of the 23rd Annual ACM Symposium on Applied Computing, pp. 1297-1301. (Acceptance rate: 24%, 9 out of 38 submissions for BIO track)	Conference or Workshop Proceedings Articles	2008
29	Jiao Li, Xiaoyan Zhu, and Jake Y. Chen* (2008) Mining Disease-Specific Molecular Association Profiles from Biomedical Literature: A Case Study. Proceedings of the 23rd Annual ACM Symposium on Applied Computing, pp. 1287-1291. (Acceptance rate: 24%, 9 out of 38 submissions for BIO track)	Conference or Workshop Proceedings Articles	2008
30	Jake Y. Chen*, Changyu Shen, Zhong Yan, Dawn P. G. Brown, and Mu Wang (2006) A Systems Biology Case Study of Ovarian Cancer Drug Resistance. Computational Systems Bioinformatics: CSB2006 Conference Proceedings, edited by Peter Markstein and Ying Xu, Series on Advances in Bioinformatics and Computational Biology, Vol. 4, pp. 389-398. (Acceptance rate: 19%, out of 154 submissions)	Conference or Workshop Proceedings Articles	2006
31	Jake Y. Chen*, Sarah L. Pinkerton, Changyu Shen, and Mu Wang (2006) An Integrated Computational Proteomics Method to Extract Protein Targets for Fanconi Anemia Studies. Proceedings of the 21st Annual ACM Symposium on Applied Computing, Dijon, France, Vol. I, pp. 173-179. (Acceptance rate: 32%)	Conference or Workshop Proceedings Articles	2006
32	Jake Y. Chen*, Changyu Shen, and Andrey Sivachenko (2006) Mining Alzheimer Disease Relevant Proteins from Integrated Protein Interactome Data. Pacific Symposium on Biocomputing, Vol. 11, pp. 367-378. (Acceptance rate: 20%)	Conference or Workshop Proceedings Articles	2006
33	Changyu Shen, Lang Li, Jake Y. Chen* (2005) Discover True Association Rates in Multi-protein Complex Proteomics Data Sets. Proceedings of the IEEE Computational Systems Biology Bioinformatics Conference, Stanford University, Stanford, CA, Vol. 1, pp. 167-174. (Acceptance rate: 12%, out of 246 submissions)	Conference or Workshop Proceedings Articles	2005
34	Jake Y. Chen*, John Carlis, and Ning Gao (2005) A Method for Conquering Complex Biological Database Queries. Proceedings of the 20th Annual ACM Symposium on Applied Computing, Santa Fe, NM, Vol. I, pp. 110-114. (Acceptance rate: 35%)	Conference or Workshop Proceedings Articles	2005
35	Jake Y. Chen* (2004) Experience with Processing and Exploration of High-throughput Protein Interaction Data, Session on Systems Biology and Bioinformatics. Proceedings of the Eighth World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, FL, Vol. VII, pp. 41-45.	Conference or Workshop Proceedings Articles	2004
36	Jake Y. Chen*, Andrey Y. Sivachenko, and Lang Li (2004) High-throughput Protein Interactome Data: Minable or Not? Fourth ACM SIGKDD Workshop on Data Mining in Bioinformatics (BioKDD 2004) at the International Conference on Knowledge Discovery and Data Mining, Seattle, WA, pp. 18-23. (Acceptance rate: 38%)	Conference or Workshop Proceedings Articles	2004
37	Jake Y. Chen*, Andrey Y. Sivachenko, Russell Bell, Connie Kurschner, Irene Ota, and Sudhir Sahasrabudhe (2003) Initial Large-scale Exploration of Protein-protein Interactions in the Human Brain. Proceedings of the IEEE CSB Bioinformatics Conference, Stanford University, Stanford, CA, published by IEEE Computer Society Press, pp. 229-234. (Acceptance rate: 18%)	Conference or Workshop Proceedings Articles	2003
38	Jake Y. Chen* and John Carlis (2003) Similar_Join: Extending DBMS with a Bio-specific Operator. Proceedings of the 18th ACM Symposium on Applied Computing, Melbourne, Florida, pp. 109-114. (Acceptance rate: 33%)	Conference or Workshop Proceedings Articles	2003
39	Jake Y. Chen* and John Carlis (2002) Managing Bioinformatics Challenges in Expression Microarray Sequence Selection Projects. Proceedings of the Second Chinese Conference on Bioinformatics, Beijing, China.	Conference or Workshop Proceedings Articles	2002
40	Jake Y. Chen* and John Carlis (2002) A High-density Microarray Case Study of Query Modeling In Bioinformatics. Proceedings of the International Conference on Bioinformatics 2002, Bangkok, Thailand.	Conference or Workshop Proceedings Articles	2002
41	Yue Chen*, Libby Shoop, John Carlis, and John Riedl (2001) A High-throughput System to Resolve Inconsistent Reading Frame Predictions for Expressed Sequence Tags. Proceedings of Workshop on Inconsistency in Data and Knowledge at the International Joint Conference on Artificial Intelligence (IJCAI), Seattle, WA.	Conference or Workshop Proceedings Articles	2001

Number	Name	Type	Year
1	Xin Gao, Jake Y. Chen, and Mohammed Zaki, ed. (2018) Special Issue on BIOKDD Workshop 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics. Published by IEEE press.	Edited Conference Proceedings and Journal Special Issues	2018
2	Sarath Chandra Janga, Dongxiao Zhu, Jake Y. Chen, and Mohammed J. Zaki, ed. (2015) IEEE/ACM Transactions on Computational Biology and Bioinformatics. Published by IEEE press.	Edited Conference Proceedings and Journal Special Issues	2015
3	Jake Y. Chen and Vasant Honavar, ed. (2014) Proceedings of IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences. IEEE.	Edited Conference Proceedings and Journal Special Issues	2014
4	Sarath Janga, Dongxiao Zhu, Jake Y. Chen, and Mohammed Zaki, ed. (2014) Proceedings of the Thirteenth International Workshop on Data Mining in Bioinformatics. ACM 2014.	Edited Conference Proceedings and Journal Special Issues	2014
5	Gaurav Pandey, Huzefa Rangwala, George Karypis, Jake Y. Chen, and Mohammed Zaki, ed. (2013) Proceedings of the Twelfth International Workshop on Data Mining in Bioinformatics. ACM 2013, ISBN 978-1-4503-2327-7.	Edited Conference Proceedings and Journal Special Issues	2013
6	Vincent S. Tseng, Hui-Huang Hsu, and Jake Y. Chen, ed. (2012) Special Issue on Data Mining for Biomarker Discovery, International Journal of Data Mining in Bioinformatics.	Edited Conference Proceedings and Journal Special Issues	2012
7	Tamer Kahveci, Saeed Salem, Mehmet Koyuturk, Jake Y. Chen, and Mohammed Zaki, ed. (2012) Proceedings of the Eleventh International Workshop on Data Mining in Bioinformatics. ACM 2012, ISBN 978-1-4503-1552-4.	Edited Conference Proceedings and Journal Special Issues	2012
8	Jake Y. Chen, Mohammed Zaki, Mohammad Hasan, and Jun Huan, ed. (2011) Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics. Published by ACM SIGKDD.	Edited Conference Proceedings and Journal Special Issues	2011
9	Vincent S. Tseng, Hui-Huang Hsu, and Jake Y. Chen, ed. (2010) Proceedings of the Second International Workshop on Data Mining in Bioinformatics. Published by IEEE press.	Edited Conference Proceedings and Journal Special Issues	2010
10	Luke Huan, Jake Y. Chen, and Mohammed Zaki, ed. (2010) Proceedings of the Ninth International Workshop on Data Mining in Bioinformatics. Published by ACM SIGKDD.	Edited Conference Proceedings and Journal Special Issues	2010
11	Vincent S. Tseng, Hui-Huang Hsu, and Jake Y. Chen, ed. (2009) Proceedings of the International Workshop on Data Mining in Bioinformatics. 34 pages. Published by IEEE press.	Edited Conference Proceedings and Journal Special Issues	2009
12	Stefano Lonardi and Jake Y. Chen, ed. (2008) Special Issue on Data Mining in Bioinformatics (BIOKDD 2007), Journal of Bioinformatics and Computational Biology, Vol. 6, No. 6.	Edited Conference Proceedings and Journal Special Issues	2008
13	Stefano Lonardi, Jake Y. Chen, and Mohammed Zaki, ed. (2008) Proceedings of the Eighth International Workshop on Data Mining in Bioinformatics. 70 pages. Published by ACM SIGKDD.	Edited Conference Proceedings and Journal Special Issues	2008
14	Amandeep S. Sidhu, Tharam S. Dillon, Elizabeth Chang and Jake Y. Chen, ed. (2007) Special Issue on Ontologies for Bioinformatics, International Journal of Bioinformatics Research and Applications, Vol. 3, No. 3.	Edited Conference Proceedings and Journal Special Issues	2007
15	Jake Y. Chen, Stefano Lonardi, and Mohammed Zaki, ed. (2007) Proceedings of the Seventh International Workshop on Data Mining in Bioinformatics. 87 pages. Published by ACM SIGKDD.	Edited Conference Proceedings and Journal Special Issues	2007
16	Katsuhisa Horimoto, Jake Chen, and Amy Chan, ed. (2004) Applications of Informatics and Cybernetics in Science and Engineering, Proceedings of the 8th World Multi-Conference on Systemics, Cybernetics and Informatics (Vol. VII). ISBN: 980-6560-13-2.	Edited Conference Proceedings and Journal Special Issues	2004

Number	Name	Type	Year
1	Jake Y. Chen and Stefano Lonardi, ed. (2009) Biological Data Mining. 656 pages. Published by Chapman & Hall/CRC, USA. ISBN: 978-1420086843.	Book	2009
2	Jake Y. Chen and Amandeep Sidhu, ed. (2007) Biological Database Modeling. 224 pages. Published by Artech House, Boston, MA, USA. ISBN: 978-1596932586.	Book	2007
3	Fan Zhang, Xiaogang Wu, and Jake Y. Chen* (2013) Computational Biomarker Discovery. Approaches in Integrative Bioinformatics – Towards Virtual Cell, pp. 355-386	Refereed – Chapters	2013
4	Jake Y. Chen, Heng Xu, Pan Shi, Adam Culbertson, and Eric Meslin (2011) Ethics and Privacy Issues for Systems Biology Application in Predictive and Personalized Medicine. Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, pp. 1-27.	Refereed – Chapters	2011
5	Fan Zhang and Jake Y. Chen* (2011) Data Mining Methods in Omics-based Biomarker Discovery. Bioinformatics for Omics data: Methods and Protocols, pp. 511-526.	Refereed – Chapters	2011
6	Jake Y. Chen* and Tianxiao Huan (2010) ProteoLens: A Database-driven Visual Data Mining Tool for Network Biology. Systems Biology for Signaling Networks, pp. 857-876.	Refereed – Chapters	2010
7	Jieun Jeong and Jake Y. Chen* (2010) Techniques for Prioritization of Disease Genes. Computational Intelligence and Pattern Analysis in Biological Informatics, pp. 309-324.	Refereed – Chapters	2010
8	Jake Y. Chen*, Shailaja Taduri, and Frank Lloyd (2009) Design of an Online Physician-Mediated Personal Health Record System. Biomedical Data and Applications, Studies in Computational Intelligence, Vol. 224, pp. 265-279.	Refereed – Chapters	2009
9	Jake Y. Chen, Eunseog Youn, Sean D Mooney* (2009) Connecting Protein Interaction Data, Mutations and Disease using Bioinformatics. Methods in Molecular Biology: Computational Systems Biology, pp. 449-462.	Refereed – Chapters	2009
10	Xiaogang Wu and Jake Y. Chen* (2009) Molecular Interaction Networks: Topological and Functional Characterizations. Automation in Genomics and Proteomics: An Engineering Case-Based Approach, pp. 145-175.	Refereed – Chapters	2009
11	Amandeep Sidhu and Jake Y. Chen* (2007) Basic Concepts. Biological Database Modeling. Published by Artech House, pp. 1-8.	Refereed – Chapters	2007
12	Zhong Yan, Jake Y. Chen*, Josh Heyen, Lee W Ott, Cary Woods, Maureen A Harrington, and Mark G Goebl (2007) Data Management in Expression-based Proteomics. Biological Database Modeling. Published by Artech House, pp. 143-162.	Refereed – Chapters	2007
13	SudhaRani Mamidipalli and Jake Y. Chen* (2007) Protein-Protein Interactions: Concepts, Databases, Software Tools, and Biomedical Implications. Current Topics in Human Genetics: Studies of Complex Diseases. Published by World Scientific Publishing Co., pp. 539-562.	Refereed – Chapters	2007
14	Sangwoo Kim, Jake Y. Chen, Vincenzo Cutello, Doheon Lee (2016) DTMBIO 2016: The Tenth International Workshop on Data and Text Mining in Biomedical Informatics. Proceedings of ACM 2016 Conference on Information and Knowledge Management, pp. 2511-2512.	Editorial Articles in Books/Proceedings	2016
15	Mohammad Al Hasan, Luke Huan, Jake Y. Chen, and Mohammed Zaki (2011) BIOKDD’11: Workshop on Data Mining in Bioinformatics. Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics, pp. 1-3.	Editorial Articles in Books/Proceedings	2011
16	Luke Huan, Jake Y. Chen, and Mohammed Zaki (2010) BIOKDD’10: Workshop on Data Mining in Bioinformatics. Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics, pp. 1-3.	Editorial Articles in Books/Proceedings	2010
17	Vincent S. Tseng, Hui-Huang Hsu, and Jake Y. Chen (2009) Data Mining for Biomarker Discovery: The Time is Ripe. IEEE International Conference on Bioinformatics and Biomedicine Workshop 2009, pp. 195-196.	Editorial Articles in Books/Proceedings	2009
18	Jake Y. Chen and Stefano Lonardi (2009) Preface. Biological Data Mining, pp. ix-xii.	Editorial Articles in Books/Proceedings	2009
19	Stefano Lonardi, Jake Y. Chen, and Mohammed Zaki (2008) Editorial: 2008 International Workshop on Data Mining in Bioinformatics. Proceedings of the Eighth International Workshop on Data Mining in Bioinformatics, pp. 1-3.	Editorial Articles in Books/Proceedings	2008
20	Jake Y. Chen and Amandeep Sidhu (2007) Preface. Biological Database Modeling, pp. vii-x.	Editorial Articles in Books/Proceedings	2007
21	Jake Y. Chen, Stefano Lonardi, and Mohammed Zaki (2007) Editorial: BIOKDD ‘07: Workshop on Data Mining in Bioinformatics. Proceedings of the Seventh International Workshop on Data Mining in Bioinformatics, pp. 1-3.	Editorial Articles in Books/Proceedings	2007
22	Jake Y. Chen and Bradley S. Sherman (2005) Session Editorial: Computer Infrastructure for Systems Biology. Proceedings of the 18th International Conference on Systems Engineering. Published by IEEE Computer Society Press, pp. 283-285.	Editorial Articles in Books/Proceedings	2005
23	Warren T. Jones, Mathew J. Palakal, and Jake Y. Chen (2004) Editorial Message: Special Track on Bioinformatics. Proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus, p. 101.	Editorial Articles in Books/Proceedings	2004